{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Otto商品分类——线性SVM\n",
    "\n",
    "我们以Kaggle 2015年举办的Otto Group Product Classification Challenge竞赛数据为例，分别调用\n",
    "缺省参数LinearSVC、\n",
    "LinearSVC + CV进行参数调优（手动实现循环）。\n",
    "\n",
    "Otto数据集是著名电商Otto提供的一个多类商品分类问题，类别数=9. 每个样本有93维数值型特征（整数，表示某种事件发生的次数，已经进行过脱敏处理）。 竞赛官网：https://www.kaggle.com/c/otto-group-product-classification-challenge/data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "#竞赛的评价指标为logloss，但LinearSVC不支持概率\n",
    "#所以在这个例子中我们用正确率accuracy_score作为模型选择的度量\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 读取数据\n",
    "# path to where the data lies\n",
    "dpath = './data/'\n",
    "\n",
    "# 采用原始特征 + tf_idf特征\n",
    "#原始特征 + tf_idf特征对线性SVM训练还是很快，RBF核已慢得不行\n",
    "# RBF核只用tf_idf特征\n",
    "train1 = pd.read_csv(dpath +\"Otto_FE_train_org.csv\")\n",
    "train2 = pd.read_csv(dpath +\"Otto_FE_train_tfidf.csv\")\n",
    "#train = pd.read_csv(dpath +\"Otto_FE_train_tfidf.csv\")\n",
    "\n",
    "#去掉多余的id\n",
    "train2 = train2.drop([\"id\",\"target\"], axis=1)\n",
    "train =  pd.concat([train1, train2], axis = 1, ignore_index=False)\n",
    "train.head()\n",
    "\n",
    "del train1\n",
    "del train2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 将类别字符串变成数字\n",
    "# drop ids and get labels\n",
    "y_train = train['target']   #形式为Class_x\n",
    "X_train = train.drop([\"id\", \"target\"], axis=1)\n",
    "\n",
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns \n",
    "\n",
    "#sklearn的学习器大多之一稀疏数据输入，模型训练会快很多\n",
    "from scipy.sparse import csr_matrix\n",
    "X_train = csr_matrix(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wuhuan/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/model_selection/_split.py:2179: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 训练样本6w+，交叉验证太慢，用train_test_split估计模型性能\n",
    "# SVM对大样本数据集支持不太好\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train_part, X_val, y_train_part, y_val = train_test_split(X_train, y_train, train_size = 10000,random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 186)\n"
     ]
    }
   ],
   "source": [
    "print (X_train_part.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 默认参数的 SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#LinearSVC不能得到每类的概率（只有predict函数，没有predict_proba函数），在Otto数据集要求输出每类的概率，这里只是示意SVM的使用方法\n",
    "#https://xacecask2.gitbooks.io/scikit-learn-user-guide-chinese-version/content/sec1.4.html\n",
    "#1.4.1.2. 得分与概率\n",
    "#1. 生成学习器实例\n",
    "SVC1 = LinearSVC()\n",
    "\n",
    "#2. 模型训练\n",
    "SVC1.fit(X_train_part, y_train_part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy is:  0.7596283588418983\n",
      "Classification report for classifier LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
      "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
      "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
      "     verbose=0):\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class_1       0.63      0.30      0.41      1615\n",
      "     Class_2       0.65      0.86      0.74     13479\n",
      "     Class_3       0.51      0.33      0.40      6706\n",
      "     Class_4       0.72      0.14      0.23      2267\n",
      "     Class_5       0.94      0.96      0.95      2312\n",
      "     Class_6       0.93      0.93      0.93     11883\n",
      "     Class_7       0.71      0.61      0.65      2399\n",
      "     Class_8       0.84      0.92      0.88      7077\n",
      "     Class_9       0.79      0.86      0.83      4140\n",
      "\n",
      "   micro avg       0.76      0.76      0.76     51878\n",
      "   macro avg       0.75      0.66      0.67     51878\n",
      "weighted avg       0.75      0.76      0.74     51878\n",
      "\n",
      "\n",
      "Confusion matrix:\n",
      "[[  487    64    14     8     8   140    48   382   464]\n",
      " [   15 11626  1435    61    74    43   102    68    55]\n",
      " [    5  4209  2223    26     9    16   155    37    26]\n",
      " [    0  1356   386   308    17   114    62    17     7]\n",
      " [    3    71     4     0  2224     3     0     4     3]\n",
      " [   71   118    24    12     4 11006   166   270   212]\n",
      " [   57   260   193     7    18   171  1462   200    31]\n",
      " [   72    84    35     2     6   207    51  6494   126]\n",
      " [   69    73     6     5    10   158    23   218  3578]]\n"
     ]
    }
   ],
   "source": [
    "#3. 在校验集上测试，估计模型性能\n",
    "y_predict = SVC1.predict(X_val)\n",
    "\n",
    "print(\"accuracy is: \",accuracy_score(y_val, y_predict))\n",
    "\n",
    "print(\"Classification report for classifier %s:\\n%s\\n\"\n",
    "      % (SVC1, classification_report(y_val, y_predict)))\n",
    "print(\"Confusion matrix:\\n%s\" % confusion_matrix(y_val, y_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性SVM正则参数调优"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性SVM LinearSVC的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） \n",
    "\n",
    "采用交叉验证，网格搜索步骤与Logistic回归正则参数处理类似，在此略。\n",
    "\n",
    "这里我们用校验集（X_val、y_val）来估计模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#单组超参数情况，模型在训练集上训练，在校验集上的测试的测试性能\n",
    "def fit_grid_point_Linear(C, X_train, y_train, X_val, y_val):\n",
    "    \n",
    "    # 在训练集上训练SVC\n",
    "    SVC2 =  LinearSVC( C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC2.score(X_val, y_val)\n",
    "    \n",
    "    print(\"C= {} : accuracy= {} \" .format(C, accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 0.1 : accuracy= 0.750183121939936 \n",
      "C= 1.0 : accuracy= 0.7596283588418983 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wuhuan/anaconda3/envs/tensorflow/lib/python3.6/site-packages/sklearn/svm/base.py:931: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 10.0 : accuracy= 0.7618836501021627 \n",
      "C= 100.0 : accuracy= 0.759454874898801 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 1000.0 : accuracy= 0.7261652338178033 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "#SVM太慢，每次只调一个参数（这里只调C，penalty为‘l2'）\n",
    "C_s = np.logspace(-1, 3, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份  \n",
    "#penalty_s = ['l1','l2']\n",
    "\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "#    for j, penalty in enumerate(penalty_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train_part, y_train_part, X_val, y_val)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "#for j, penalty in enumerate(penalty_s):\n",
    "plt.plot(x_axis, np.array(accuracy_s), 'b-')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "#plt.savefig('SVM_Otto.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n"
     ]
    }
   ],
   "source": [
    "### 最佳超参数\n",
    "index = np.argmax(accuracy_s, axis=None)\n",
    "Best_C = C_s[ index ]\n",
    "\n",
    "print(Best_C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在该模型中，超参数C用于控制损失项的影响，C越大，损失项对模型的影响越大。当C从0.1增长到10的时候，精度持续增加；当C继续增加，精度呈现急速下降的趋势，训练误差增大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 找到最佳参数后，用全体训练数据训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SVC训练SVC，支持概率输出\n",
    "Best_C = 10\n",
    "\n",
    "SVC3 = LinearSVC(C = Best_C)\n",
    "SVC3.fit(X_train, y_train)\n",
    "\n",
    "#保持模型，用于后续测试\n",
    "import pickle\n",
    "pickle.dump(SVC3, open(\"Otto_LinearSVC.pkl\", 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "1. 为什么要引入核函数？\n",
    "当我们在解决线性不可分的问题时，我们需要通过一个映射函数，把样本值映射到更高维的空间或者无穷维。\n",
    "在特征空间中，我们对线性可分的新样本使用前面提到过的求解线性可分的情况下的分类问题的方法时，需要计算样本内积，\n",
    "但是因为样本维数很高，容易造成“维数灾难”，所以这里我们就引入了核函数，把高维向量的内积转变成了求低维向量的内积问题。"
   ]
  },
  {
   "attachments": {
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     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. 给出合页损失的数学形式并画出图形。\n",
    "![%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202019-05-04%20%E4%B8%8B%E5%8D%881.48.14.png](attachment:%E5%B1%8F%E5%B9%95%E5%BF%AB%E7%85%A7%202019-05-04%20%E4%B8%8B%E5%8D%881.48.14.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "什么是支持向量？为什么SVM中只有一小部分的训练样本是支持向量（稀疏的）？\n",
    "过两类样本中离分类面最近的点且平行于分类面的训练样本就叫做支持向量。因为在分类面以外的、被正确分类的样本，不会对分类面产生影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. 决策树中特征分裂的准则有哪些？\n",
    "ID3算法是以信息增益为准则选择分裂后信息增益最大的属性进行分裂。\n",
    "C4.5算法是ID3算法的一种改进。它是以信息增益率为准则选择属性。\n",
    "CART使用GINI系数来选择划分属性：在所有候选属性中，选择划分后GINI系数最小的属性作为优先划分属性。而回归树使用用最小平方差。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. SVM模型并没有概率解释。为了使SVM模型能输出概率，我们应该设置哪个参数？\n",
    "probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 186)\n"
     ]
    }
   ],
   "source": [
    "X_train_part2, X_val2, y_train_part2, y_val2 = train_test_split(X_train, y_train, train_size = 10000,random_state = 0)\n",
    "print (X_train_part2.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gamma, X_train2, y_train2, X_val2, y_val2):\n",
    "    \n",
    "    # 在训练集是那个利用SVC训练\n",
    "    SVC4 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC4 = SVC4.fit(X_train2, y_train2)\n",
    "    \n",
    "    # 在校验集上返回accuracy\n",
    "    accuracy = SVC4.score(X_val2, y_val2)\n",
    "    \n",
    "    print(\"C= {} and gamma = {}: accuracy= {} \" .format(C, gamma, accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_s = np.matrix(np.zeros(shape=(5, 3)), float)\n",
    "gamma_s = np.logspace(-1, 1, 3)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 0.1 and gamma = 0.1: accuracy= 0.7108022668568564 \n",
      "C= 0.1 and gamma = 1.0: accuracy= 0.7350321909094414 \n",
      "C= 0.1 and gamma = 10.0: accuracy= 0.3282316203400285 \n"
     ]
    }
   ],
   "source": [
    "oneC = 0.1\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[0,j] = fit_grid_point_RBF(oneC, gamma, X_train_part2, y_train_part2, X_val2, y_val2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 1 and gamma = 0.1: accuracy= 0.7491614942750299 \n",
      "C= 1 and gamma = 1.0: accuracy= 0.7792898723929218 \n",
      "C= 1 and gamma = 10.0: accuracy= 0.5959944485138209 \n"
     ]
    }
   ],
   "source": [
    "oneC = 1\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[1,j] = fit_grid_point_RBF(oneC, gamma, X_train_part2, y_train_part2, X_val2, y_val2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 10 and gamma = 0.1: accuracy= 0.7726974825552257 \n",
      "C= 10 and gamma = 1.0: accuracy= 0.79181926828328 \n",
      "C= 10 and gamma = 10.0: accuracy= 0.6120320752534794 \n"
     ]
    }
   ],
   "source": [
    "oneC = 10\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[1,j] = fit_grid_point_RBF(oneC, gamma, X_train_part2, y_train_part2, X_val2, y_val2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 100 and gamma = 0.1: accuracy= 0.7842245267743552 \n",
      "C= 100 and gamma = 1.0: accuracy= 0.7804849840009253 \n",
      "C= 100 and gamma = 10.0: accuracy= 0.6113766914684452 \n"
     ]
    }
   ],
   "source": [
    "oneC = 100\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[1,j] = fit_grid_point_RBF(oneC, gamma, X_train_part2, y_train_part2, X_val2, y_val2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C= 1000 and gamma = 0.1: accuracy= 0.7726974825552257 \n",
      "C= 1000 and gamma = 1.0: accuracy= 0.7750106017965226 \n",
      "C= 1000 and gamma = 10.0: accuracy= 0.6113766914684452 \n"
     ]
    }
   ],
   "source": [
    "oneC = 1000\n",
    "\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    accuracy_s[1,j] = fit_grid_point_RBF(oneC, gamma, X_train_part2, y_train_part2, X_val2, y_val2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从上述结果会发现，gamma参数非常重要(当gamma=0.01或gamma=100时性能很差),非线性模型比线性模型性能好（注意我们这里只用了tfidf特征）。 但速度慢了不是一点半点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Otto_SVM_result = pd.read_csv(\"Otto_SVM_result.csv\")\n",
    "accuracy_s1 = Otto_SVM_result['accuracy']\n",
    "\n",
    "C_s = np.logspace(-1, 3, 5)# logspace(a,b,N)把10的a次方到10的b次方区间分成N份 \n",
    "gamma_s = np.logspace(-1, 1, 3)  \n",
    "accuracy_s1 =np.array(accuracy_s1).reshape(len(C_s),len(gamma_s))\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "for j, gamma in enumerate(gamma_s):\n",
    "    plt.plot(x_axis, np.array(accuracy_s1[:,j]), label = ' Test - log(gamma)' + str(np.log10(gamma)))\n",
    "\n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.savefig('RBF_SVM_Otto.png' )\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "### 最佳超参数\n",
    "index = np.unravel_index(np.argmax(accuracy_s1, axis=None), accuracy_s1.shape)\n",
    "Best_C = C_s[ index[0] ]\n",
    "Best_gamma = gamma_s[ index[1] ]\n",
    "\n",
    "print(Best_C)\n",
    "print(Best_gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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